Abstract
We presented neural network based classifier for diabetic retinopathy detection in fundus images. The Multi Layer Perception Neural Network (MLPNN) based classifier is used to categorize fundus retinal images as normal and abnormal. Feature vector is composed of transform domain features and different statistical parameters of fundus retinal images. 64-point Fast Fourier Transform (FFT) is used as transform domain feature and Entropy, mean, standard deviation, average, Euler number, contrast, correlation, energy and homogeneity are used as statistical parameters. This feature vector is used as input to MLPNN based classifier. The classifier performance is calculated on DIARETDB0 database. The % classification accuracy on train and CV data sets is 99.48% and 100 respectively.
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